7 papers
Effective Biological Representation Learning by Masking Gene Expression
Kian Kenyon-Dean, Alina Selega, Ihab Bendidi +5
RNA sequencing produces rich and diverse datasets of gene expression, offering compelling insights into cellular state and function that have many applications in drug discovery. M…
Towards scientific discovery with dictionary learning: Extracting biological concepts from microscopy foundation models
Konstantin Donhauser, Kristina Ulicna, Gemma Elyse Moran +4
Sparse dictionary learning (DL) has emerged as a powerful approach to extract semantically meaningful concepts from the internals of large language models (LLMs) trained mainly in…
ViTally Consistent: Scaling Biological Representation Learning for Cell Microscopy
Kian Kenyon-Dean, Zitong Jerry Wang, John Urbanik +10
Large-scale cell microscopy screens are used in drug discovery and molecular biology research to study the effects of millions of chemical and genetic perturbations on cells. To us…
RxRx3-core: Benchmarking drug-target interactions in High-Content Microscopy
Oren Kraus, Federico Comitani, John Urbanik +6
High Content Screening (HCS) microscopy datasets have transformed the ability to profile cellular responses to genetic and chemical perturbations, enabling cell-based inference of…
A Cross Modal Knowledge Distillation & Data Augmentation Recipe for Improving Transcriptomics Representations through Morphological Features
Ihab Bendidi, Yassir El Mesbahi, Alisandra K. Denton +4
Understanding cellular responses to stimuli is crucial for biological discovery and drug development. Transcriptomics provides interpretable, gene-level insights, while microscopy…
Benchmarking Transcriptomics Foundation Models for Perturbation Analysis : one PCA still rules them all
Ihab Bendidi, Shawn Whitfield, Kian Kenyon-Dean +4
Understanding the relationships among genes, compounds, and their interactions in living organisms remains limited due to technological constraints and the complexity of biological…